Scilab Function
Last update : 12/1/2004

findAC - discrete-time system subspace identification

### Calling Sequence

  `[A,C] = findAC(S,N,L,R,METH,TOL,PRINTW)`
  `[A,C,RCND] = findAC(S,N,L,R,METH,TOL,PRINTW)`

### Parameters

- `S`: integer, the number of block rows in the block-Hankel matrices
- `N`: integer
- `L`: integer
- `R`: matrix, relevant part of the R factor of the concatenated block-Hankel matrices computed by a call to findr.
- `METH`: integer, an option for the method to use
  - `= 1`: MOESP method with past inputs and outputs;
  - `= 2`: N4SID method;

  Default: METH = 3.

- `TOL`: the tolerance used for estimating the rank of matrices. If TOL > 0, then the given value of TOL is used as a lower bound for the reciprocal condition number. Default: prod(size(matrix))*epsilon_machine where epsilon_machine is the relative machine precision.
- `PRINTW`: integer, switch for printing the warning messages.
  - `PRINTW`= 1: print warning messages;
  - `= 0`: do not print warning messages.

  Default: PRINTW = 0.

- `A`: matrix, state system matrix
- `C`: matrix, output system matrix
- `RCND`: vector of length 4, condition numbers of the matrices involved in rank decision

### Description

finds the system matrices A and C of a discrete-time system, given the system order and the relevant part of the R factor of the concatenated block-Hankel matrices, using subspace identification techniques (MOESP or N4SID).

  *[A,C] = findAC(S,N,L,R,METH,TOL,PRINTW) computes the system matrices A and C. The model structure is: x(k+1) = Ax(k) + Bu(k) + Ke(k), k >= 1, y(k) = Cx(k) + Du(k) + e(k), where x(k) and y(k) are vectors of length N and L, respectively.
  *[A,C,RCND] = findAC(S,N,L,R,METH,TOL,PRINTW) also returns the vector RCND of length 4 containing the condition numbers of the matrices involved in rank decisions.

Matrix R, computed by findR, should be determined with suitable arguments METH and JOBD.

### Examples

    //generate data from a given linear system
    A = [ 0.5, 0.1,-0.1, 0.2;
          0.1, 0,  -0.1,-0.1;
         -0.4,-0.6,-0.7,-0.1;
          0.8, 0,  -0.6,-0.6];
    B = [0.8;0.1;1;-1];
    C = [1 2 -1 0];
    SYS=syslin(0.1,A,B,C);
    nsmp=100;
    U=prbs_a(nsmp,nsmp/5);
    Y=(flts(U,SYS)+0.3*rand(1,nsmp,'normal'));

    // Compute R
    S=15;L=1;
    [R,N,SVAL] = findR(S,Y',U');

    N=3;
    METH=3;TOL=-1;
    [A,C] = findAC(S,N,L,R,METH,TOL);

### See Also

`findABCD`, `findBD`, `findBDK`, `findR`, `sorder`, `sident`,